{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_parent</th>\n",
       "      <th>product_title</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_headline</th>\n",
       "      <th>review_body</th>\n",
       "      <th>review_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>572944212</td>\n",
       "      <td>mary meyer wubbanub plush pacifier, lamb</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Love this!</td>\n",
       "      <td>Perfect match for the Gund Huggybuddy I bought...</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>911821018</td>\n",
       "      <td>wubbanub lamb infant pacifier</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Love 💕</td>\n",
       "      <td>My little girl love this paci contraption!</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>392768822</td>\n",
       "      <td>wubbanub infant pacifier - giraffe</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>My son loves this one and will only sleep if h...</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>392768822</td>\n",
       "      <td>wubbanub infant pacifier - giraffe</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Perfect</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>911821018</td>\n",
       "      <td>wubbanub lamb infant pacifier</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Amazing addition to the nursery!</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18934</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not for bottle liners</td>\n",
       "      <td>We have been using the bottle warmer and have ...</td>\n",
       "      <td>2004-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18935</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good enough for me</td>\n",
       "      <td>This isn't the greatest product ever invented,...</td>\n",
       "      <td>2004-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18936</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>I love it!</td>\n",
       "      <td>I love this bottle warmer.  After researching ...</td>\n",
       "      <td>2004-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18937</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>save your money</td>\n",
       "      <td>I finally broke down and opened this shower gi...</td>\n",
       "      <td>2003-12-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18938</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Always makes bottles too hot</td>\n",
       "      <td>We bought this bottle warmer two weeks ago bec...</td>\n",
       "      <td>2003-04-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11049 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      product_parent                                      product_title  \\\n",
       "0          572944212           mary meyer wubbanub plush pacifier, lamb   \n",
       "1          911821018                      wubbanub lamb infant pacifier   \n",
       "2          392768822                 wubbanub infant pacifier - giraffe   \n",
       "3          392768822                 wubbanub infant pacifier - giraffe   \n",
       "4          911821018                      wubbanub lamb infant pacifier   \n",
       "...              ...                                                ...   \n",
       "18934       51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18935       51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18936       51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18937       51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18938       51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "\n",
       "       star_rating  helpful_votes  total_votes  vine  verified_purchase  \\\n",
       "0              5.0            0.0          0.0   0.0                1.0   \n",
       "1              5.0            0.0          0.0   0.0                1.0   \n",
       "2              5.0            0.0          0.0   0.0                1.0   \n",
       "3              5.0            0.0          0.0   0.0                1.0   \n",
       "4              5.0            0.0          0.0   0.0                1.0   \n",
       "...            ...            ...          ...   ...                ...   \n",
       "18934          2.0            0.0          0.0   0.0                0.0   \n",
       "18935          4.0            1.0          1.0   0.0                0.0   \n",
       "18936          5.0            0.0          0.0   0.0                0.0   \n",
       "18937          1.0            2.0          2.0   0.0                0.0   \n",
       "18938          2.0            0.0          0.0   0.0                0.0   \n",
       "\n",
       "                    review_headline  \\\n",
       "0                        Love this!   \n",
       "1                            Love 💕   \n",
       "2                        Five Stars   \n",
       "3                        Five Stars   \n",
       "4                        Five Stars   \n",
       "...                             ...   \n",
       "18934         Not for bottle liners   \n",
       "18935            good enough for me   \n",
       "18936                    I love it!   \n",
       "18937               save your money   \n",
       "18938  Always makes bottles too hot   \n",
       "\n",
       "                                             review_body review_date  \n",
       "0      Perfect match for the Gund Huggybuddy I bought...  2015-08-31  \n",
       "1             My little girl love this paci contraption!  2015-08-31  \n",
       "2      My son loves this one and will only sleep if h...  2015-08-31  \n",
       "3                                                Perfect  2015-08-31  \n",
       "4                       Amazing addition to the nursery!  2015-08-31  \n",
       "...                                                  ...         ...  \n",
       "18934  We have been using the bottle warmer and have ...  2004-05-24  \n",
       "18935  This isn't the greatest product ever invented,...  2004-04-04  \n",
       "18936  I love this bottle warmer.  After researching ...  2004-04-04  \n",
       "18937  I finally broke down and opened this shower gi...  2003-12-02  \n",
       "18938  We bought this bottle warmer two weeks ago bec...  2003-04-27  \n",
       "\n",
       "[11049 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product='pacifier'#hair_dryer microwave pacifier\n",
    "train=pd.read_csv(product+'.tsv', sep='\\t', header=0)\n",
    "# 删除无关字段\n",
    "train=train.drop(columns=['marketplace','customer_id','review_id','product_id','product_category'])\n",
    "train['product_title'] = train['product_title'].str.lower()\n",
    "# 将“n\",\"y\"转换成”0\",\"1\"\n",
    "train.replace('n','0',inplace=True)\n",
    "train.replace('y','1',inplace=True)\n",
    "train.replace('N','0',inplace=True)\n",
    "train.replace('Y','1',inplace=True)\n",
    "# 将字符串类型的数据转换成float\n",
    "train[['star_rating','helpful_votes','total_votes','vine','verified_purchase']]\\\n",
    "    =train[['star_rating','helpful_votes','total_votes','vine','verified_purchase']].astype('float')\n",
    "train['product_parent']=train['product_parent'].astype('object')\n",
    "# 清除表中无关的产品\n",
    "train=train[train['product_title'].str.contains(product)]     ##!!!product\n",
    "train.loc[:,'review_date']=pd.to_datetime(train.loc[:,'review_date'],format='%m/%d/%Y', errors='coerce')\n",
    "train=train.dropna(subset=train.columns,how='any')\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
       "      <th>verified_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "      <td>11049.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.396868</td>\n",
       "      <td>0.594714</td>\n",
       "      <td>0.863698</td>\n",
       "      <td>0.006335</td>\n",
       "      <td>0.880623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.118145</td>\n",
       "      <td>4.366193</td>\n",
       "      <td>5.122051</td>\n",
       "      <td>0.079346</td>\n",
       "      <td>0.324247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        star_rating  helpful_votes   total_votes          vine  \\\n",
       "count  11049.000000   11049.000000  11049.000000  11049.000000   \n",
       "mean       4.396868       0.594714      0.863698      0.006335   \n",
       "std        1.118145       4.366193      5.122051      0.079346   \n",
       "min        1.000000       0.000000      0.000000      0.000000   \n",
       "25%        4.000000       0.000000      0.000000      0.000000   \n",
       "50%        5.000000       0.000000      0.000000      0.000000   \n",
       "75%        5.000000       0.000000      0.000000      0.000000   \n",
       "max        5.000000     209.000000    223.000000      1.000000   \n",
       "\n",
       "       verified_purchase  \n",
       "count       11049.000000  \n",
       "mean            0.880623  \n",
       "std             0.324247  \n",
       "min             0.000000  \n",
       "25%             1.000000  \n",
       "50%             1.000000  \n",
       "75%             1.000000  \n",
       "max             1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 描述性统计\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df1=train.groupby([\"product_parent\"],sort=True).size().reset_index(name='total_rates_count')\n",
    "df2=train.groupby([\"product_parent\",'star_rating'],sort=True).size().reset_index(name='stars_rate_count')\n",
    "merge12 = pd.merge(df1, df2, on='product_parent', how='outer')\n",
    "# 计算每种商品的每一级评价的比例\n",
    "merge12['rate_ratio']=merge12['stars_rate_count']/merge12['total_rates_count']\n",
    "# merge12=merge12.set_index(['product_parent','star_rating'])\n",
    "merge12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
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   },
   "outputs": [
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       "      <td>0.808017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>392768822</td>\n",
       "      <td>520</td>\n",
       "      <td>5.0</td>\n",
       "      <td>411</td>\n",
       "      <td>0.790385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>246038397</td>\n",
       "      <td>833</td>\n",
       "      <td>5.0</td>\n",
       "      <td>627</td>\n",
       "      <td>0.752701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_parent  total_rates_count  star_rating  stars_rate_count  \\\n",
       "810       816382986                  1          5.0                 1   \n",
       "264       352182356                  1          5.0                 1   \n",
       "202       254455198                  1          5.0                 1   \n",
       "831       843386126                  1          5.0                 1   \n",
       "530       516813595                  1          5.0                 1   \n",
       "..              ...                ...          ...               ...   \n",
       "809       812583172                259          5.0               213   \n",
       "597       572944212                493          5.0               373   \n",
       "427       450475749                474          5.0               383   \n",
       "355       392768822                520          5.0               411   \n",
       "189       246038397                833          5.0               627   \n",
       "\n",
       "     rate_ratio  \n",
       "810    1.000000  \n",
       "264    1.000000  \n",
       "202    1.000000  \n",
       "831    1.000000  \n",
       "530    1.000000  \n",
       "..          ...  \n",
       "809    0.822394  \n",
       "597    0.756592  \n",
       "427    0.808017  \n",
       "355    0.790385  \n",
       "189    0.752701  \n",
       "\n",
       "[279 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算每种商品的五星好评比例\n",
    "most_rated_5=merge12.loc[merge12.loc[:,'star_rating']==5,:].sort_values(by='stars_rate_count')\n",
    "most_rated_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 描述性统计，查看商品整体的评价比例\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "tempx=merge12.loc[merge12['product_parent']==most_rated_5.iloc[-1]['product_parent']]['star_rating']\n",
    "tempy=merge12.loc[merge12['product_parent']==most_rated_5.iloc[-1]['product_parent']]['rate_ratio']\n",
    "merge12.loc[merge12['product_parent']==most_rated_5.iloc[-1]['product_parent']].plot(x='star_rating',\n",
    "                                                                                     y='rate_ratio',kind='bar')\n",
    "tempy = tempy.cumsum()\n",
    "tempy /= tempy.iloc[-1]\n",
    "plt.plot(tempx-1,tempy)\n",
    "fig=plt.gcf()\n",
    "fig.savefig('star_rating_ratio'+product+'.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_date</th>\n",
       "      <th>rates_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-04-27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-12-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-04-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-05-24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-06-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>2015-08-27</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>2015-08-28</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>2015-08-29</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     review_date  rates_count\n",
       "0     2003-04-27            1\n",
       "1     2003-12-02            1\n",
       "2     2004-04-04            2\n",
       "3     2004-05-24            1\n",
       "4     2004-06-20            1\n",
       "...          ...          ...\n",
       "1889  2015-08-27           19\n",
       "1890  2015-08-28            9\n",
       "1891  2015-08-29            9\n",
       "1892  2015-08-30           10\n",
       "1893  2015-08-31           15\n",
       "\n",
       "[1894 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每天的评价数量\n",
    "df3 = train.groupby([\"review_date\"],sort=True)[\"star_rating\"].size().reset_index(name='rates_count')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'savefig'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-19-776eaaf60aa2>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     10\u001B[0m \u001B[1;31m#标题\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     11\u001B[0m \u001B[0mplt\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtitle\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m'Changes in total_rates_count with time'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 12\u001B[1;33m \u001B[0mfig\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msavefig\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m'changes_in_total_sales'\u001B[0m\u001B[1;33m+\u001B[0m\u001B[0mproduct\u001B[0m\u001B[1;33m+\u001B[0m\u001B[1;34m'.png'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     13\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     14\u001B[0m \u001B[0mplt\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mshow\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mAttributeError\u001B[0m: 'list' object has no attribute 'savefig'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计商品的总评价数随时间的变化\n",
    "#x坐标轴上点的数值\n",
    "x=df3.loc[:,'review_date']\n",
    "#y坐标轴上点的数值\n",
    "y=df3.loc[:,'rates_count']\n",
    "fig=plt.plot(x,y)\n",
    "#x轴文本\n",
    "plt.xlabel('review_date')\n",
    "#y轴文本\n",
    "plt.ylabel('rates_count')\n",
    "#标题\n",
    "plt.title('Changes in total_rates_count with time')\n",
    "fig.savefig('changes_in_total_sales'+product+'.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_parent</th>\n",
       "      <th>product_title</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_headline</th>\n",
       "      <th>review_body</th>\n",
       "      <th>review_date</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>review_headline_body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12275</th>\n",
       "      <td>450475749</td>\n",
       "      <td>wubbanub brown monkey pacifier</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Handy and cute</td>\n",
       "      <td>Worked wonderfully for keeping pacifier in bab...</td>\n",
       "      <td>2013-11-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Handy and cuteWorked wonderfully for keeping p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11940</th>\n",
       "      <td>24160750</td>\n",
       "      <td>nuk 2 pack classic silicone bpa free fashion p...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>not for babies with big cheeks</td>\n",
       "      <td>My baby loves this pacifer but I don't give it...</td>\n",
       "      <td>2013-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>not for babies with big cheeksMy baby loves th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12048</th>\n",
       "      <td>892018890</td>\n",
       "      <td>mam night orthodontic pacifier, boy, 0-6 plus ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Love these</td>\n",
       "      <td>It glows at night so you don't have to turn on...</td>\n",
       "      <td>2013-12-13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Love theseIt glows at night so you don't have ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12044</th>\n",
       "      <td>671647927</td>\n",
       "      <td>safety 1st pacifier medicine dispenser</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not good!</td>\n",
       "      <td>The npple is very hard!  My daughter did not l...</td>\n",
       "      <td>2013-12-13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Not good!The npple is very hard!  My daughter ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12043</th>\n",
       "      <td>981045906</td>\n",
       "      <td>personalized pacifiers pacifier</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>the color may appear a little different in per...</td>\n",
       "      <td>its cute! its not exactly the same color as sh...</td>\n",
       "      <td>2013-12-13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>the color may appear a little different in per...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18804</th>\n",
       "      <td>957640647</td>\n",
       "      <td>dexbaby womb sounds bear audio pacifier, brown...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>wonderful bear</td>\n",
       "      <td>excellent addition to any baby's nursery. the ...</td>\n",
       "      <td>2007-06-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wonderful bearexcellent addition to any baby's...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18932</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Excellent Bottle Warmer</td>\n",
       "      <td>We registered for this gift after reading the ...</td>\n",
       "      <td>2005-02-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Excellent Bottle WarmerWe registered for this ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18934</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not for bottle liners</td>\n",
       "      <td>We have been using the bottle warmer and have ...</td>\n",
       "      <td>2004-05-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not for bottle linersWe have been using the bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18936</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>I love it!</td>\n",
       "      <td>I love this bottle warmer.  After researching ...</td>\n",
       "      <td>2004-04-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I love it!I love this bottle warmer.  After re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18938</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Always makes bottles too hot</td>\n",
       "      <td>We bought this bottle warmer two weeks ago bec...</td>\n",
       "      <td>2003-04-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Always makes bottles too hotWe bought this bot...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11049 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_parent                                      product_title  \\\n",
       "12275       450475749                     wubbanub brown monkey pacifier   \n",
       "11940        24160750  nuk 2 pack classic silicone bpa free fashion p...   \n",
       "12048       892018890  mam night orthodontic pacifier, boy, 0-6 plus ...   \n",
       "12044       671647927             safety 1st pacifier medicine dispenser   \n",
       "12043       981045906                    personalized pacifiers pacifier   \n",
       "...               ...                                                ...   \n",
       "18804       957640647  dexbaby womb sounds bear audio pacifier, brown...   \n",
       "18932        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18934        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18936        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18938        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "\n",
       "       star_rating  helpful_votes  total_votes  vine  verified_purchase  \\\n",
       "12275          5.0            1.0          1.0   0.0                1.0   \n",
       "11940          2.0            2.0          2.0   0.0                1.0   \n",
       "12048          5.0            1.0          1.0   0.0                1.0   \n",
       "12044          1.0            1.0          1.0   0.0                0.0   \n",
       "12043          5.0            2.0          2.0   0.0                1.0   \n",
       "...            ...            ...          ...   ...                ...   \n",
       "18804          5.0            0.0          0.0   0.0                0.0   \n",
       "18932          5.0            0.0          0.0   0.0                0.0   \n",
       "18934          2.0            0.0          0.0   0.0                0.0   \n",
       "18936          5.0            0.0          0.0   0.0                0.0   \n",
       "18938          2.0            0.0          0.0   0.0                0.0   \n",
       "\n",
       "                                         review_headline  \\\n",
       "12275                                     Handy and cute   \n",
       "11940                     not for babies with big cheeks   \n",
       "12048                                         Love these   \n",
       "12044                                          Not good!   \n",
       "12043  the color may appear a little different in per...   \n",
       "...                                                  ...   \n",
       "18804                                     wonderful bear   \n",
       "18932                            Excellent Bottle Warmer   \n",
       "18934                              Not for bottle liners   \n",
       "18936                                         I love it!   \n",
       "18938                       Always makes bottles too hot   \n",
       "\n",
       "                                             review_body review_date  \\\n",
       "12275  Worked wonderfully for keeping pacifier in bab...  2013-11-27   \n",
       "11940  My baby loves this pacifer but I don't give it...  2013-12-27   \n",
       "12048  It glows at night so you don't have to turn on...  2013-12-13   \n",
       "12044  The npple is very hard!  My daughter did not l...  2013-12-13   \n",
       "12043  its cute! its not exactly the same color as sh...  2013-12-13   \n",
       "...                                                  ...         ...   \n",
       "18804  excellent addition to any baby's nursery. the ...  2007-06-27   \n",
       "18932  We registered for this gift after reading the ...  2005-02-06   \n",
       "18934  We have been using the bottle warmer and have ...  2004-05-24   \n",
       "18936  I love this bottle warmer.  After researching ...  2004-04-04   \n",
       "18938  We bought this bottle warmer two weeks ago bec...  2003-04-27   \n",
       "\n",
       "       helpful_rate                               review_headline_body  \n",
       "12275           1.0  Handy and cuteWorked wonderfully for keeping p...  \n",
       "11940           1.0  not for babies with big cheeksMy baby loves th...  \n",
       "12048           1.0  Love theseIt glows at night so you don't have ...  \n",
       "12044           1.0  Not good!The npple is very hard!  My daughter ...  \n",
       "12043           1.0  the color may appear a little different in per...  \n",
       "...             ...                                                ...  \n",
       "18804           NaN  wonderful bearexcellent addition to any baby's...  \n",
       "18932           NaN  Excellent Bottle WarmerWe registered for this ...  \n",
       "18934           NaN  Not for bottle linersWe have been using the bo...  \n",
       "18936           NaN  I love it!I love this bottle warmer.  After re...  \n",
       "18938           NaN  Always makes bottles too hotWe bought this bot...  \n",
       "\n",
       "[11049 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计商品每个分数的评价数量随时间的变化\n",
    "plt.figure()\n",
    "reviews_grp = train.groupby([train['review_date'].dt.year, train['review_date'].dt.month, train['star_rating']]).count()['product_parent'].unstack().fillna(0)\n",
    "bar = reviews_grp.plot(figsize=(15,8), rot=45, colormap='jet')\n",
    "fig=bar.get_figure()\n",
    "fig.savefig('5_changes_in_total_sales'+product+'.png')\n",
    "# 统计每条评价的”有用“投票比例\n",
    "train['helpful_votes'].fillna(0)\n",
    "train['total_votes'].fillna(0)\n",
    "train['helpful_rate']=train['helpful_votes']/train['total_votes']\n",
    "train.sort_values(by='helpful_rate',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 将评价标题和正文内容连起来\n",
    "train['review_headline_body']=train['review_headline']+train['review_body']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 将缺失值填充为0\n",
    "train=train.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 输出清洁完毕的数据\n",
    "train.to_csv('cleaned_'+product+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_parent</th>\n",
       "      <th>product_title</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_headline</th>\n",
       "      <th>review_body</th>\n",
       "      <th>review_date</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>review_headline_body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>572944212</td>\n",
       "      <td>mary meyer wubbanub plush pacifier, lamb</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Love this!</td>\n",
       "      <td>Perfect match for the Gund Huggybuddy I bought...</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Love this!Perfect match for the Gund Huggybudd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>911821018</td>\n",
       "      <td>wubbanub lamb infant pacifier</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Love 💕</td>\n",
       "      <td>My little girl love this paci contraption!</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Love 💕My little girl love this paci contraption!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>392768822</td>\n",
       "      <td>wubbanub infant pacifier - giraffe</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>My son loves this one and will only sleep if h...</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Five StarsMy son loves this one and will only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>392768822</td>\n",
       "      <td>wubbanub infant pacifier - giraffe</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Perfect</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Five StarsPerfect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>911821018</td>\n",
       "      <td>wubbanub lamb infant pacifier</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Amazing addition to the nursery!</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Five StarsAmazing addition to the nursery!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18934</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not for bottle liners</td>\n",
       "      <td>We have been using the bottle warmer and have ...</td>\n",
       "      <td>2004-05-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not for bottle linersWe have been using the bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18935</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good enough for me</td>\n",
       "      <td>This isn't the greatest product ever invented,...</td>\n",
       "      <td>2004-04-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>good enough for meThis isn't the greatest prod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18936</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>I love it!</td>\n",
       "      <td>I love this bottle warmer.  After researching ...</td>\n",
       "      <td>2004-04-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>I love it!I love this bottle warmer.  After re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18937</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>save your money</td>\n",
       "      <td>I finally broke down and opened this shower gi...</td>\n",
       "      <td>2003-12-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>save your moneyI finally broke down and opened...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18938</th>\n",
       "      <td>51313971</td>\n",
       "      <td>munchkin deluxe bottle  and food warmer with p...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Always makes bottles too hot</td>\n",
       "      <td>We bought this bottle warmer two weeks ago bec...</td>\n",
       "      <td>2003-04-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Always makes bottles too hotWe bought this bot...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11049 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_parent                                      product_title  \\\n",
       "0           572944212           mary meyer wubbanub plush pacifier, lamb   \n",
       "1           911821018                      wubbanub lamb infant pacifier   \n",
       "2           392768822                 wubbanub infant pacifier - giraffe   \n",
       "3           392768822                 wubbanub infant pacifier - giraffe   \n",
       "4           911821018                      wubbanub lamb infant pacifier   \n",
       "...               ...                                                ...   \n",
       "18934        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18935        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18936        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18937        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "18938        51313971  munchkin deluxe bottle  and food warmer with p...   \n",
       "\n",
       "       star_rating  helpful_votes  total_votes  vine  verified_purchase  \\\n",
       "0              5.0            0.0          0.0   0.0                1.0   \n",
       "1              5.0            0.0          0.0   0.0                1.0   \n",
       "2              5.0            0.0          0.0   0.0                1.0   \n",
       "3              5.0            0.0          0.0   0.0                1.0   \n",
       "4              5.0            0.0          0.0   0.0                1.0   \n",
       "...            ...            ...          ...   ...                ...   \n",
       "18934          2.0            0.0          0.0   0.0                0.0   \n",
       "18935          4.0            1.0          1.0   0.0                0.0   \n",
       "18936          5.0            0.0          0.0   0.0                0.0   \n",
       "18937          1.0            2.0          2.0   0.0                0.0   \n",
       "18938          2.0            0.0          0.0   0.0                0.0   \n",
       "\n",
       "                    review_headline  \\\n",
       "0                        Love this!   \n",
       "1                            Love 💕   \n",
       "2                        Five Stars   \n",
       "3                        Five Stars   \n",
       "4                        Five Stars   \n",
       "...                             ...   \n",
       "18934         Not for bottle liners   \n",
       "18935            good enough for me   \n",
       "18936                    I love it!   \n",
       "18937               save your money   \n",
       "18938  Always makes bottles too hot   \n",
       "\n",
       "                                             review_body review_date  \\\n",
       "0      Perfect match for the Gund Huggybuddy I bought...  2015-08-31   \n",
       "1             My little girl love this paci contraption!  2015-08-31   \n",
       "2      My son loves this one and will only sleep if h...  2015-08-31   \n",
       "3                                                Perfect  2015-08-31   \n",
       "4                       Amazing addition to the nursery!  2015-08-31   \n",
       "...                                                  ...         ...   \n",
       "18934  We have been using the bottle warmer and have ...  2004-05-24   \n",
       "18935  This isn't the greatest product ever invented,...  2004-04-04   \n",
       "18936  I love this bottle warmer.  After researching ...  2004-04-04   \n",
       "18937  I finally broke down and opened this shower gi...  2003-12-02   \n",
       "18938  We bought this bottle warmer two weeks ago bec...  2003-04-27   \n",
       "\n",
       "       helpful_rate                               review_headline_body  \n",
       "0               0.0  Love this!Perfect match for the Gund Huggybudd...  \n",
       "1               0.0   Love 💕My little girl love this paci contraption!  \n",
       "2               0.0  Five StarsMy son loves this one and will only ...  \n",
       "3               0.0                                  Five StarsPerfect  \n",
       "4               0.0         Five StarsAmazing addition to the nursery!  \n",
       "...             ...                                                ...  \n",
       "18934           0.0  Not for bottle linersWe have been using the bo...  \n",
       "18935           1.0  good enough for meThis isn't the greatest prod...  \n",
       "18936           0.0  I love it!I love this bottle warmer.  After re...  \n",
       "18937           1.0  save your moneyI finally broke down and opened...  \n",
       "18938           0.0  Always makes bottles too hotWe bought this bot...  \n",
       "\n",
       "[11049 rows x 12 columns]"
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